Exploration of patterns predicting renal damage in patients with diabetes type II using a visual temporal analysis laboratory

J Am Med Inform Assoc. 2015 Mar;22(2):275-89. doi: 10.1136/amiajnl-2014-002927. Epub 2014 Oct 28.

Abstract

Objective: To analyze the longitudinal data of multiple patients and to discover new temporal knowledge, we designed and developed the Visual Temporal Analysis Laboratory (ViTA-Lab). In this study, we demonstrate several of the capabilities of the ViTA-Lab framework through the exploration of renal-damage risk factors in patients with diabetes type II.

Materials and methods: The ViTA-Lab framework combines data-driven temporal data mining techniques, with interactive, query-driven, visual analytical capabilities, to support, in an integrated fashion, an iterative investigation of time-oriented clinical data and of patterns discovered in them. Patterns discovered through the data mining mode can be explored visually, and vice versa. Both analysis modes are supported by a rich underlying ontology of clinical concepts, their relations, and their temporal properties. The knowledge enables us to apply a temporal-abstraction pre-processing phase that abstracts in a context-sensitive manner raw time-stamped data into interval-based clinically meaningful interpretations, increasing the results' significance. We demonstrate our approach through the exploration of risk factors associated with future renal damage (micro-albuminuria and macro-albuminuria) and their relationship to the hemoglobin A1C (HbA1C ) and creatinine level concepts, in the longitudinal records of 22 000 patients with diabetes type II followed for up to 5 years.

Results: The iterative ViTA-Lab analysis process was highly feasible. Higher ranges of either normal albuminuria or normal creatinine values and their combination were shown to be significantly associated with future micro-albuminuria and macro-albuminuria. The risk increased given high HbA1C levels for women in the lower range of normal albuminuria, and for men in the higher range of albuminuria.

Conclusions: The ViTA-Lab framework can potentially serve as a virtual laboratory for investigations of large masses of longitudinal clinical databases, for discovery of new knowledge through interactive exploration, clustering, classification, and prediction.

Keywords: Visual Analytics; data analysis; knowledge discovery; ontologies; temporal abstraction; temporal data mining.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Albuminuria / complications*
  • Creatinine / blood
  • Data Display*
  • Data Mining*
  • Diabetes Mellitus, Type 2 / blood
  • Diabetes Mellitus, Type 2 / complications*
  • Diabetic Nephropathies / etiology*
  • Female
  • Glycated Hemoglobin A / analysis*
  • Humans
  • Male
  • Pattern Recognition, Automated*
  • Risk Factors
  • User-Computer Interface*

Substances

  • Glycated Hemoglobin A
  • hemoglobin A1c protein, human
  • Creatinine